Multi-modal trajectory forecasting methods commonly evaluate using single-agent metrics
(marginal metrics), such as minimum Average Displacement Error (ADE) and Final …

In this paper, we present LookOut, a novel autonomy system that perceives the environment,
predicts a diverse set of futures of how the scene might unroll and estimates the trajectory of …

Simulation is a crucial tool for accelerating the development of autonomous vehicles.
Making simulation realistic requires models of the human road users who interact with such …

Understanding the interaction between multiple agents is crucial for realistic vehicle
trajectory prediction. Existing methods have attempted to infer the interaction from the …

Robust multi-agent trajectory prediction is essential for the safe control of robotic systems. A
major challenge is to efficiently learn a representation that approximates the true joint …

Trajectory prediction is one of the basic tasks in the autonomous driving field, which aims to
predict the future position of other agents around the ego vehicle so that a safe yet efficient …

We present MotionDiffuser, a diffusion based representation for the joint distribution of future
trajectories over multiple agents. Such representation has several key advantages: first, our …

The reasonable trajectory prediction of surrounding traffic participants is crucial for
autonomous driving. Especially, how to predict multiple plausible trajectories is still a …

Motion forecasting is an essential task for autonomous driving, and the effective information
utilization from infrastructure and other vehicles can enhance motion forecasting …

Motion forecasting for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …